#pragma once

#include <memory>
#include <limits>
#include <functional>

#include "ATen/ATenGeneral.h"
#include "ATen/Allocator.h"
#include "ATen/Generator.h"
#include "ATen/Scalar.h"
#include "ATen/ScalarType.h"
#include "ATen/SparseTensorRef.h"
#include "ATen/Tensor.h"
#include "ATen/core/ArrayRef.h"
#include "ATen/core/Half.h"

// To solve the conflict of s_addr in inaddr.h
#ifdef _MSC_VER
#ifdef s_addr
#undef s_addr
#endif
#endif

namespace at {

class Context;
struct Storage;
struct Generator;
struct Allocator;

// Note [Empty versus 0-dim tensors]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Unlike Torch, ATen treats zero-dimension tensors as having ONE
// element (that is to say, a zero-dimensional tensor is a scalar!)
// This is in contrast to Torch, where a zero-dimension tensor has
// zero elements.
//
// Because we are backed by Torch tensors, we need to be able to
// represent this state (of numel==0).  These tensors are represented
// by one-dimensional tensors with size[0] == 0 and stride[0] == 1
// (the stride is arbitrary but matches the NumPy equivalent).
constexpr std::array<int64_t, 1> kEmptySizes { {0} };
constexpr std::array<int64_t, 1> kEmptyStrides { {1} };

static inline void noop_deleter(void*) {}

enum class TypeID {
  CPUByte,
  CPUChar,
  CPUDouble,
  CPUFloat,
  CPUInt,
  CPULong,
  CPUShort,
  CPUHalf,
  SparseCPUByte,
  SparseCPUChar,
  SparseCPUDouble,
  SparseCPUFloat,
  SparseCPUInt,
  SparseCPULong,
  SparseCPUShort,
  CUDAByte,
  CUDAChar,
  CUDADouble,
  CUDAFloat,
  CUDAInt,
  CUDALong,
  CUDAShort,
  CUDAHalf,
  SparseCUDAByte,
  SparseCUDAChar,
  SparseCUDADouble,
  SparseCUDAFloat,
  SparseCUDAInt,
  SparseCUDALong,
  SparseCUDAShort,
  Undefined,
  NumOptions
};


struct AT_API Type {
  explicit Type(Context * context)
  : context(context) {}
  virtual ~Type() {}
  virtual ScalarType scalarType() const = 0;
  virtual Backend backend() const = 0;
  virtual bool is_cuda() const = 0;
  virtual bool is_sparse() const = 0;
  virtual bool is_distributed() const = 0;
  static void registerAll(Context * context);
  virtual std::unique_ptr<Storage> storage() const = 0;
  virtual std::unique_ptr<Storage> storage(size_t size) const = 0;
  virtual std::unique_ptr<Storage> storageFromBlob(void * data, int64_t size, const std::function<void(void*)> & deleter=noop_deleter) const = 0;
  virtual std::unique_ptr<Storage> storageWithAllocator(int64_t size, std::unique_ptr<Allocator> allocator) const = 0;
  virtual std::unique_ptr<Generator> generator() const = 0;
  virtual Tensor unsafeTensorFromTH(void * th_pointer, bool retain) const = 0;
  virtual std::unique_ptr<Storage> unsafeStorageFromTH(void * th_pointer, bool retain) const = 0;
  virtual const char * toString() const = 0;
  virtual size_t elementSizeInBytes() const = 0;
  virtual Type & toBackend(Backend b) const;
  virtual Type & toScalarType(ScalarType s) const;
  Context& get_context() const { return *context; }

  // contingious IDs for all types in the system
  // for external dispatch
  virtual TypeID ID() const = 0;

  Tensor copy(const Tensor & src, bool non_blocking=false) const;
  Tensor & copy_(Tensor & self, const Tensor & src, bool non_blocking=false) const;
  virtual Tensor & s_copy_(Tensor & self, const Tensor & src, bool non_blocking) const = 0;

  Tensor tensorFromBlob(void * data, IntList sizes, const std::function<void(void*)> & deleter=noop_deleter) const;
  Tensor tensorFromBlob(void * data, IntList sizes, IntList strides, const std::function<void(void*)> & deleter=noop_deleter) const;
  Tensor tensorWithAllocator(IntList sizes, std::unique_ptr<Allocator> allocator) const;
  Tensor tensorWithAllocator(IntList sizes, IntList strides, std::unique_ptr<Allocator> allocator) const;
  Tensor scalarTensor(Scalar s) const;

  bool operator==(const Type& other) const;
  bool operator!=(const Type& other) const;

  // example
  // virtual Tensor * add(Tensor & a, Tensor & b) = 0;
  virtual int64_t storage_offset(const Tensor & self) const;
  virtual Tensor & resize_(Tensor & self, IntList size) const;
  virtual Tensor & zeros_out(Tensor & result, IntList size) const;
  virtual Tensor zeros(IntList size) const;
  virtual Tensor & zeros_like_out(Tensor & result, const Tensor & input) const;
  virtual Tensor zeros_like(const Tensor & input) const;
  virtual Tensor & ones_out(Tensor & result, IntList size) const;
  virtual Tensor ones(IntList size) const;
  virtual Tensor & ones_like_out(Tensor & result, const Tensor & input) const;
  virtual Tensor ones_like(const Tensor & input) const;
  virtual int64_t numel(const Tensor & self) const;
  virtual Tensor & set_(Tensor & self, Storage & source) const;
  virtual Tensor & set_(Tensor & self, Storage & source, int64_t storage_offset, IntList size, IntList stride={}) const;
  virtual Tensor & set_(Tensor & self, const Tensor & source) const;
  virtual Tensor & set_(Tensor & self) const;
  virtual Tensor & fill_(Tensor & self, Scalar value) const;
  virtual Tensor & fill_(Tensor & self, const Tensor & value) const;
  virtual bool is_contiguous(const Tensor & self) const;
  virtual bool is_set_to(const Tensor & self, const Tensor & tensor) const;
  Tensor & masked_fill_(Tensor & self, const Tensor & mask, Scalar value) const;
  virtual Tensor & s_masked_fill_(Tensor & self, const Tensor & mask, Scalar value) const;
  Tensor & masked_fill_(Tensor & self, const Tensor & mask, const Tensor & value) const;
  virtual Tensor & s_masked_fill_(Tensor & self, const Tensor & mask, const Tensor & value) const;
  Tensor & masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source) const;
  virtual Tensor & s_masked_scatter_(Tensor & self, const Tensor & mask, const Tensor & source) const;
  Tensor & masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask) const;
  virtual Tensor & s_masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask) const;
  Tensor masked_select(const Tensor & self, const Tensor & mask) const;
  virtual Tensor s_masked_select(const Tensor & self, const Tensor & mask) const;
  virtual Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1) const;
  virtual Tensor t(const Tensor & self) const;
  virtual Tensor & nonzero_out(Tensor & result, const Tensor & self) const;
  virtual Tensor nonzero(const Tensor & self) const;
  virtual Tensor contiguous(const Tensor & self) const;
  virtual Tensor clone(const Tensor & self) const;
  virtual Tensor view(const Tensor & self, IntList size) const;
  virtual Tensor & resize_as_(Tensor & self, const Tensor & the_template) const;
  virtual Tensor & index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) const;
  virtual Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index) const;
  virtual Tensor & index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source) const;
  virtual Tensor & take_out(Tensor & result, const Tensor & self, const Tensor & index) const;
  virtual Tensor take(const Tensor & self, const Tensor & index) const;
  virtual Tensor & put_(Tensor & self, const Tensor & index, const Tensor & source, bool accumulate=false) const;
  virtual Tensor & index_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source) const;
  virtual Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, Scalar value) const;
  virtual Tensor & index_fill_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & value) const;
  virtual Tensor unfold(const Tensor & self, int64_t dimension, int64_t size, int64_t step) const;
  virtual Tensor & range_out(Tensor & result, Scalar start, Scalar end, Scalar step=1) const;
  virtual Tensor range(Scalar start, Scalar end, Scalar step=1) const;
  virtual Tensor & arange_out(Tensor & result, Scalar start, Scalar end, Scalar step=1) const;
  virtual Tensor arange(Scalar start, Scalar end, Scalar step=1) const;
  virtual Tensor & arange_out(Tensor & result, Scalar end) const;
  virtual Tensor arange(Scalar end) const;
  virtual Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src) const;
  virtual Tensor & scatter_(Tensor & self, int64_t dim, const Tensor & index, Scalar value) const;
  virtual Tensor & scatter_add_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & src) const;
  virtual Tensor & gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) const;
  virtual Tensor gather(const Tensor & self, int64_t dim, const Tensor & index) const;
  virtual void* data_ptr(const Tensor & self) const;
  virtual bool equal(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __and___out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor __and__(const Tensor & self, Scalar other) const;
  Tensor & __and___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s___and___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor __and__(const Tensor & self, const Tensor & other) const;
  virtual Tensor s___and__(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __iand__(Tensor & self, Scalar other) const;
  Tensor & __iand__(Tensor & self, const Tensor & other) const;
  virtual Tensor & s___iand__(Tensor & self, const Tensor & other) const;
  virtual Tensor & __or___out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor __or__(const Tensor & self, Scalar other) const;
  Tensor & __or___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s___or___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor __or__(const Tensor & self, const Tensor & other) const;
  virtual Tensor s___or__(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __ior__(Tensor & self, Scalar other) const;
  Tensor & __ior__(Tensor & self, const Tensor & other) const;
  virtual Tensor & s___ior__(Tensor & self, const Tensor & other) const;
  virtual Tensor & __xor___out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor __xor__(const Tensor & self, Scalar other) const;
  Tensor & __xor___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s___xor___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor __xor__(const Tensor & self, const Tensor & other) const;
  virtual Tensor s___xor__(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __ixor__(Tensor & self, Scalar other) const;
  Tensor & __ixor__(Tensor & self, const Tensor & other) const;
  virtual Tensor & s___ixor__(Tensor & self, const Tensor & other) const;
  virtual Tensor & __lshift___out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor __lshift__(const Tensor & self, Scalar other) const;
  Tensor & __lshift___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s___lshift___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor __lshift__(const Tensor & self, const Tensor & other) const;
  virtual Tensor s___lshift__(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __ilshift__(Tensor & self, Scalar other) const;
  Tensor & __ilshift__(Tensor & self, const Tensor & other) const;
  virtual Tensor & s___ilshift__(Tensor & self, const Tensor & other) const;
  virtual Tensor & __rshift___out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor __rshift__(const Tensor & self, Scalar other) const;
  Tensor & __rshift___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s___rshift___out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor __rshift__(const Tensor & self, const Tensor & other) const;
  virtual Tensor s___rshift__(const Tensor & self, const Tensor & other) const;
  virtual Tensor & __irshift__(Tensor & self, Scalar other) const;
  Tensor & __irshift__(Tensor & self, const Tensor & other) const;
  virtual Tensor & s___irshift__(Tensor & self, const Tensor & other) const;
  virtual Tensor & lt_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor lt(const Tensor & self, Scalar other) const;
  Tensor & lt_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_lt_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor lt(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_lt(const Tensor & self, const Tensor & other) const;
  virtual Tensor & lt_(Tensor & self, Scalar other) const;
  Tensor & lt_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_lt_(Tensor & self, const Tensor & other) const;
  virtual Tensor & gt_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor gt(const Tensor & self, Scalar other) const;
  Tensor & gt_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_gt_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor gt(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_gt(const Tensor & self, const Tensor & other) const;
  virtual Tensor & gt_(Tensor & self, Scalar other) const;
  Tensor & gt_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_gt_(Tensor & self, const Tensor & other) const;
  virtual Tensor & le_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor le(const Tensor & self, Scalar other) const;
  Tensor & le_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_le_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor le(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_le(const Tensor & self, const Tensor & other) const;
  virtual Tensor & le_(Tensor & self, Scalar other) const;
  Tensor & le_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_le_(Tensor & self, const Tensor & other) const;
  virtual Tensor & ge_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor ge(const Tensor & self, Scalar other) const;
  Tensor & ge_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_ge_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor ge(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_ge(const Tensor & self, const Tensor & other) const;
  virtual Tensor & ge_(Tensor & self, Scalar other) const;
  Tensor & ge_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_ge_(Tensor & self, const Tensor & other) const;
  virtual Tensor & eq_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor eq(const Tensor & self, Scalar other) const;
  Tensor & eq_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_eq_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor eq(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_eq(const Tensor & self, const Tensor & other) const;
  virtual Tensor & eq_(Tensor & self, Scalar other) const;
  Tensor & eq_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_eq_(Tensor & self, const Tensor & other) const;
  virtual Tensor & ne_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor ne(const Tensor & self, Scalar other) const;
  Tensor & ne_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_ne_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor ne(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_ne(const Tensor & self, const Tensor & other) const;
  virtual Tensor & ne_(Tensor & self, Scalar other) const;
  Tensor & ne_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_ne_(Tensor & self, const Tensor & other) const;
  virtual std::tuple<Tensor &,Tensor &> min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim, bool keepdim=false) const;
  Tensor & min_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_min_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor min(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_min(const Tensor & self, const Tensor & other) const;
  virtual Tensor min(const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim, bool keepdim=false) const;
  Tensor & max_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_max_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor max(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_max(const Tensor & self, const Tensor & other) const;
  virtual Tensor max(const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) const;
  virtual std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) const;
  virtual std::tuple<Tensor &,Tensor &> mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false) const;
  virtual std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim=-1, bool keepdim=false) const;
  virtual std::tuple<Tensor &,Tensor &> median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor median(const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool descending=false) const;
  virtual std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim=-1, bool descending=false) const;
  virtual std::tuple<Tensor &,Tensor &> topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) const;
  virtual std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) const;
  virtual Tensor all(const Tensor & self) const;
  virtual Tensor any(const Tensor & self) const;
  virtual int64_t get_device(const Tensor & self) const;
  virtual Tensor & abs_out(Tensor & result, const Tensor & self) const;
  virtual Tensor abs(const Tensor & self) const;
  virtual Tensor & abs_(Tensor & self) const;
  virtual Tensor & sigmoid_(Tensor & self) const;
  virtual Tensor & sigmoid_out(Tensor & result, const Tensor & self) const;
  virtual Tensor sigmoid(const Tensor & self) const;
  virtual Tensor & log_(Tensor & self) const;
  virtual Tensor & log_out(Tensor & result, const Tensor & self) const;
  virtual Tensor log(const Tensor & self) const;
  virtual Tensor & log1p_(Tensor & self) const;
  virtual Tensor & log1p_out(Tensor & result, const Tensor & self) const;
  virtual Tensor log1p(const Tensor & self) const;
  virtual Tensor & lgamma_out(Tensor & result, const Tensor & self) const;
  virtual Tensor lgamma(const Tensor & self) const;
  virtual Tensor & lgamma_(Tensor & self) const;
  virtual Tensor & digamma_out(Tensor & result, const Tensor & self) const;
  virtual Tensor digamma(const Tensor & self) const;
  virtual Tensor & digamma_(Tensor & self) const;
  virtual Tensor & polygamma_out(Tensor & result, int64_t n, const Tensor & self) const;
  virtual Tensor polygamma(int64_t n, const Tensor & self) const;
  virtual Tensor & polygamma_(Tensor & self, int64_t n) const;
  virtual Tensor & exp_(Tensor & self) const;
  virtual Tensor & exp_out(Tensor & result, const Tensor & self) const;
  virtual Tensor exp(const Tensor & self) const;
  virtual Tensor & expm1_(Tensor & self) const;
  virtual Tensor & expm1_out(Tensor & result, const Tensor & self) const;
  virtual Tensor expm1(const Tensor & self) const;
  virtual Tensor & cos_(Tensor & self) const;
  virtual Tensor & cos_out(Tensor & result, const Tensor & self) const;
  virtual Tensor cos(const Tensor & self) const;
  virtual Tensor & acos_(Tensor & self) const;
  virtual Tensor & acos_out(Tensor & result, const Tensor & self) const;
  virtual Tensor acos(const Tensor & self) const;
  virtual Tensor & cosh_(Tensor & self) const;
  virtual Tensor & cosh_out(Tensor & result, const Tensor & self) const;
  virtual Tensor cosh(const Tensor & self) const;
  virtual Tensor & sin_(Tensor & self) const;
  virtual Tensor & sin_out(Tensor & result, const Tensor & self) const;
  virtual Tensor sin(const Tensor & self) const;
  virtual Tensor & asin_(Tensor & self) const;
  virtual Tensor & asin_out(Tensor & result, const Tensor & self) const;
  virtual Tensor asin(const Tensor & self) const;
  virtual Tensor & sinh_(Tensor & self) const;
  virtual Tensor & sinh_out(Tensor & result, const Tensor & self) const;
  virtual Tensor sinh(const Tensor & self) const;
  virtual Tensor & tan_(Tensor & self) const;
  virtual Tensor & tan_out(Tensor & result, const Tensor & self) const;
  virtual Tensor tan(const Tensor & self) const;
  virtual Tensor & atan_(Tensor & self) const;
  virtual Tensor & atan_out(Tensor & result, const Tensor & self) const;
  virtual Tensor atan(const Tensor & self) const;
  virtual Tensor & tanh_(Tensor & self) const;
  virtual Tensor & tanh_out(Tensor & result, const Tensor & self) const;
  virtual Tensor tanh(const Tensor & self) const;
  virtual Tensor & erf_(Tensor & self) const;
  virtual Tensor & erf_out(Tensor & result, const Tensor & self) const;
  virtual Tensor erf(const Tensor & self) const;
  virtual Tensor & erfc_(Tensor & self) const;
  virtual Tensor & erfc_out(Tensor & result, const Tensor & self) const;
  virtual Tensor erfc(const Tensor & self) const;
  virtual Tensor & erfinv_(Tensor & self) const;
  virtual Tensor & erfinv_out(Tensor & result, const Tensor & self) const;
  virtual Tensor erfinv(const Tensor & self) const;
  virtual Tensor & sqrt_(Tensor & self) const;
  virtual Tensor & sqrt_out(Tensor & result, const Tensor & self) const;
  virtual Tensor sqrt(const Tensor & self) const;
  virtual Tensor & rsqrt_(Tensor & self) const;
  virtual Tensor & rsqrt_out(Tensor & result, const Tensor & self) const;
  virtual Tensor rsqrt(const Tensor & self) const;
  virtual Tensor & ceil_(Tensor & self) const;
  virtual Tensor & ceil_out(Tensor & result, const Tensor & self) const;
  virtual Tensor ceil(const Tensor & self) const;
  virtual Tensor & floor_(Tensor & self) const;
  virtual Tensor & floor_out(Tensor & result, const Tensor & self) const;
  virtual Tensor floor(const Tensor & self) const;
  virtual Tensor & round_(Tensor & self) const;
  virtual Tensor & round_out(Tensor & result, const Tensor & self) const;
  virtual Tensor round(const Tensor & self) const;
  virtual Tensor & trunc_(Tensor & self) const;
  virtual Tensor & trunc_out(Tensor & result, const Tensor & self) const;
  virtual Tensor trunc(const Tensor & self) const;
  virtual Tensor & frac_(Tensor & self) const;
  virtual Tensor & frac_out(Tensor & result, const Tensor & self) const;
  virtual Tensor frac(const Tensor & self) const;
  virtual Tensor & mean_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor mean(const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor mean(const Tensor & self) const;
  virtual Tensor & var_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false) const;
  virtual Tensor var(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false) const;
  virtual Tensor var(const Tensor & self, bool unbiased=true) const;
  virtual Tensor & std_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false) const;
  virtual Tensor std(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false) const;
  virtual Tensor std(const Tensor & self, bool unbiased=true) const;
  virtual Tensor & norm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, bool keepdim=false) const;
  virtual Tensor norm(const Tensor & self, Scalar p, int64_t dim, bool keepdim=false) const;
  virtual Tensor norm(const Tensor & self, Scalar p=2) const;
  virtual Tensor & renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) const;
  virtual Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) const;
  virtual Tensor & renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) const;
  Tensor dist(const Tensor & self, const Tensor & other, Scalar p=2) const;
  virtual Tensor s_dist(const Tensor & self, const Tensor & other, Scalar p=2) const;
  virtual Tensor & reciprocal_out(Tensor & result, const Tensor & self) const;
  virtual Tensor reciprocal(const Tensor & self) const;
  virtual Tensor & reciprocal_(Tensor & self) const;
  virtual Tensor & neg_out(Tensor & result, const Tensor & self) const;
  virtual Tensor neg(const Tensor & self) const;
  virtual Tensor & neg_(Tensor & self) const;
  Tensor & atan2_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_atan2_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor atan2(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_atan2(const Tensor & self, const Tensor & other) const;
  Tensor & atan2_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_atan2_(Tensor & self, const Tensor & other) const;
  virtual Tensor & pow_out(Tensor & result, const Tensor & self, Scalar exponent) const;
  virtual Tensor pow(const Tensor & self, Scalar exponent) const;
  Tensor & pow_out(Tensor & result, const Tensor & self, const Tensor & exponent) const;
  virtual Tensor & s_pow_out(Tensor & result, const Tensor & self, const Tensor & exponent) const;
  Tensor pow(const Tensor & self, const Tensor & exponent) const;
  virtual Tensor s_pow(const Tensor & self, const Tensor & exponent) const;
  virtual Tensor & pow_out(Tensor & result, Scalar base, const Tensor & self) const;
  virtual Tensor pow(Scalar base, const Tensor & self) const;
  virtual Tensor & pow_(Tensor & self, Scalar exponent) const;
  Tensor & pow_(Tensor & self, const Tensor & exponent) const;
  virtual Tensor & s_pow_(Tensor & self, const Tensor & exponent) const;
  Tensor & lerp_out(Tensor & result, const Tensor & self, const Tensor & end, Scalar weight) const;
  virtual Tensor & s_lerp_out(Tensor & result, const Tensor & self, const Tensor & end, Scalar weight) const;
  Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight) const;
  virtual Tensor s_lerp(const Tensor & self, const Tensor & end, Scalar weight) const;
  Tensor & lerp_(Tensor & self, const Tensor & end, Scalar weight) const;
  virtual Tensor & s_lerp_(Tensor & self, const Tensor & end, Scalar weight) const;
  virtual Tensor & linspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100) const;
  virtual Tensor linspace(Scalar start, Scalar end, int64_t steps=100) const;
  virtual Tensor & logspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100) const;
  virtual Tensor logspace(Scalar start, Scalar end, int64_t steps=100) const;
  virtual Tensor & histc_out(Tensor & result, const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0) const;
  virtual Tensor histc(const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0) const;
  virtual Tensor & zero_(Tensor & self) const;
  virtual Tensor & sum_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor sum(const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor sum(const Tensor & self) const;
  virtual Tensor & prod_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor prod(const Tensor & self, int64_t dim, bool keepdim=false) const;
  virtual Tensor prod(const Tensor & self) const;
  virtual Tensor & cumsum_out(Tensor & result, const Tensor & self, int64_t dim) const;
  virtual Tensor cumsum(const Tensor & self, int64_t dim) const;
  virtual Tensor & cumprod_out(Tensor & result, const Tensor & self, int64_t dim) const;
  virtual Tensor cumprod(const Tensor & self, int64_t dim) const;
  virtual Tensor & sign_out(Tensor & result, const Tensor & self) const;
  virtual Tensor sign(const Tensor & self) const;
  virtual Tensor & sign_(Tensor & self) const;
  virtual Tensor trace(const Tensor & self) const;
  virtual Tensor & add_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1) const;
  virtual Tensor add(const Tensor & self, Scalar other, Scalar alpha=1) const;
  Tensor & add_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & s_add_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  Tensor add(const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor s_add(const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & add_out(Tensor & result, const Tensor & self, SparseTensor other, Scalar alpha=1) const;
  virtual Tensor add(const Tensor & self, SparseTensor other, Scalar alpha=1) const;
  virtual Tensor & add_(Tensor & self, Scalar other, Scalar alpha=1) const;
  Tensor & add_(Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & s_add_(Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & add_(Tensor & self, SparseTensor other, Scalar alpha=1) const;
  virtual Tensor & sub_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1) const;
  virtual Tensor sub(const Tensor & self, Scalar other, Scalar alpha=1) const;
  Tensor & sub_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & s_sub_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor s_sub(const Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & sub_(Tensor & self, Scalar other, Scalar alpha=1) const;
  Tensor & sub_(Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & s_sub_(Tensor & self, const Tensor & other, Scalar alpha=1) const;
  virtual Tensor & mul_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor mul(const Tensor & self, Scalar other) const;
  Tensor & mul_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_mul_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor mul(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_mul(const Tensor & self, const Tensor & other) const;
  virtual Tensor & mul_(Tensor & self, Scalar other) const;
  Tensor & mul_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_mul_(Tensor & self, const Tensor & other) const;
  virtual Tensor & div_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor div(const Tensor & self, Scalar other) const;
  Tensor & div_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_div_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor div(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_div(const Tensor & self, const Tensor & other) const;
  virtual Tensor & div_(Tensor & self, Scalar other) const;
  Tensor & div_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_div_(Tensor & self, const Tensor & other) const;
  virtual Tensor & fmod_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor fmod(const Tensor & self, Scalar other) const;
  Tensor & fmod_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_fmod_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor fmod(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_fmod(const Tensor & self, const Tensor & other) const;
  virtual Tensor & fmod_(Tensor & self, Scalar other) const;
  Tensor & fmod_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_fmod_(Tensor & self, const Tensor & other) const;
  virtual Tensor & remainder_out(Tensor & result, const Tensor & self, Scalar other) const;
  virtual Tensor remainder(const Tensor & self, Scalar other) const;
  Tensor & remainder_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  virtual Tensor & s_remainder_out(Tensor & result, const Tensor & self, const Tensor & other) const;
  Tensor remainder(const Tensor & self, const Tensor & other) const;
  virtual Tensor s_remainder(const Tensor & self, const Tensor & other) const;
  virtual Tensor & remainder_(Tensor & self, Scalar other) const;
  Tensor & remainder_(Tensor & self, const Tensor & other) const;
  virtual Tensor & s_remainder_(Tensor & self, const Tensor & other) const;
  virtual Tensor & clamp_out(Tensor & result, const Tensor & self, Scalar min, Scalar max) const;
  virtual Tensor clamp(const Tensor & self, Scalar min, Scalar max) const;
  virtual Tensor & clamp_(Tensor & self, Scalar min, Scalar max) const;
  virtual Tensor & clamp_min_out(Tensor & result, const Tensor & self, Scalar min) const;
  virtual Tensor clamp_min(const Tensor & self, Scalar min) const;
  virtual Tensor & clamp_min_(Tensor & self, Scalar min) const;
  virtual Tensor & clamp_max_out(Tensor & result, const Tensor & self, Scalar max) const;
  virtual Tensor clamp_max(const Tensor & self, Scalar max) const;
  virtual Tensor & clamp_max_(Tensor & self, Scalar max) const;
  virtual Tensor _dot(const Tensor & self, const Tensor & tensor) const;
  virtual Tensor & tril_out(Tensor & result, const Tensor & self, int64_t diagonal=0) const;
  virtual Tensor tril(const Tensor & self, int64_t diagonal=0) const;
  virtual Tensor & tril_(Tensor & self, int64_t diagonal=0) const;
  virtual Tensor & triu_out(Tensor & result, const Tensor & self, int64_t diagonal=0) const;
  virtual Tensor triu(const Tensor & self, int64_t diagonal=0) const;
  virtual Tensor & triu_(Tensor & self, int64_t diagonal=0) const;
  virtual Tensor & cross_out(Tensor & result, const Tensor & self, const Tensor & other, int64_t dim=-1) const;
  virtual Tensor cross(const Tensor & self, const Tensor & other, int64_t dim=-1) const;
  virtual Tensor & eye_out(Tensor & result, int64_t n, int64_t m=-1) const;
  virtual Tensor eye(int64_t n, int64_t m=-1) const;
  virtual Tensor & diag_out(Tensor & result, const Tensor & self, int64_t diagonal=0) const;
  virtual Tensor diag(const Tensor & self, int64_t diagonal=0) const;
  Tensor & addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & s_addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor s_addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addmm_out(Tensor & result, const Tensor & self, SparseTensor mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor addmm(const Tensor & self, SparseTensor mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addmm_(Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addmm_(Tensor & self, SparseTensor mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  Tensor & _addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & s__addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  Tensor _addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor s__addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & _addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  Tensor & _addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & s__addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor s__addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & _addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & _ger_out(Tensor & result, const Tensor & self, const Tensor & vec2) const;
  virtual Tensor _ger(const Tensor & self, const Tensor & vec2) const;
  virtual Tensor & _mv_out(Tensor & result, const Tensor & self, const Tensor & vec) const;
  virtual Tensor _mv(const Tensor & self, const Tensor & vec) const;
  virtual Tensor & _mm_out(Tensor & result, const Tensor & self, const Tensor & mat2) const;
  virtual Tensor _mm(const Tensor & self, const Tensor & mat2) const;
  virtual Tensor & bmm_out(Tensor & result, const Tensor & self, const Tensor & mat2) const;
  virtual Tensor bmm(const Tensor & self, const Tensor & mat2) const;
  Tensor & addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & s_addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor s_addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  Tensor & baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & s_baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor s_baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & baddbmm_(Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1) const;
  Tensor & addcmul_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor & s_addcmul_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor s_addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  Tensor & addcmul_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor & s_addcmul_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  Tensor & addcdiv_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor & s_addcdiv_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor s_addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  Tensor & addcdiv_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual Tensor & s_addcdiv_(Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1) const;
  virtual std::tuple<Tensor &,Tensor &> gesv_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A) const;
  virtual std::tuple<Tensor,Tensor> gesv(const Tensor & self, const Tensor & A) const;
  virtual std::tuple<Tensor &,Tensor &> gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A) const;
  virtual std::tuple<Tensor,Tensor> gels(const Tensor & self, const Tensor & A) const;
  virtual std::tuple<Tensor &,Tensor &> trtrs_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) const;
  virtual std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) const;
  virtual std::tuple<Tensor &,Tensor &> symeig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false, bool upper=true) const;
  virtual std::tuple<Tensor,Tensor> symeig(const Tensor & self, bool eigenvectors=false, bool upper=true) const;
  virtual std::tuple<Tensor &,Tensor &> eig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false) const;
  virtual std::tuple<Tensor,Tensor> eig(const Tensor & self, bool eigenvectors=false) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> svd_out(Tensor & res1, Tensor & res2, Tensor & res3, const Tensor & self, bool some=true) const;
  virtual std::tuple<Tensor,Tensor,Tensor> svd(const Tensor & self, bool some=true) const;
  virtual Tensor & inverse_out(Tensor & output, const Tensor & self) const;
  virtual Tensor inverse(const Tensor & self) const;
  virtual Tensor & potrf_out(Tensor & output, const Tensor & self, bool upper=true) const;
  virtual Tensor potrf(const Tensor & self, bool upper=true) const;
  virtual Tensor & potrs_out(Tensor & result, const Tensor & self, const Tensor & input2, bool upper=true) const;
  virtual Tensor potrs(const Tensor & self, const Tensor & input2, bool upper=true) const;
  virtual Tensor & potri_out(Tensor & output, const Tensor & self, bool upper=true) const;
  virtual Tensor potri(const Tensor & self, bool upper=true) const;
  virtual std::tuple<Tensor &,Tensor &> pstrf_out(Tensor & res1, Tensor & res2, const Tensor & self, bool upper=true, Scalar tol=-1) const;
  virtual std::tuple<Tensor,Tensor> pstrf(const Tensor & self, bool upper=true, Scalar tol=-1) const;
  virtual std::tuple<Tensor &,Tensor &> qr_out(Tensor & res1, Tensor & res2, const Tensor & self) const;
  virtual std::tuple<Tensor,Tensor> qr(const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self) const;
  virtual std::tuple<Tensor,Tensor> geqrf(const Tensor & self) const;
  virtual Tensor & orgqr_out(Tensor & result, const Tensor & self, const Tensor & input2) const;
  virtual Tensor orgqr(const Tensor & self, const Tensor & input2) const;
  virtual Tensor & ormqr_out(Tensor & result, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false) const;
  virtual Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false) const;
  virtual std::tuple<Tensor &,Tensor &> btrifact_out(Tensor & result, Tensor & pivots, const Tensor & self, bool pivot=true) const;
  virtual std::tuple<Tensor,Tensor> btrifact(const Tensor & self, bool pivot=true) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> btrifact_with_info_out(Tensor & result, Tensor & pivots, Tensor & info, const Tensor & self, bool pivot=true) const;
  virtual std::tuple<Tensor,Tensor,Tensor> btrifact_with_info(const Tensor & self, bool pivot=true) const;
  virtual Tensor & btrisolve_out(Tensor & result, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots) const;
  virtual Tensor btrisolve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots) const;
  virtual Tensor & randperm_out(Tensor & result, int64_t n, Generator * generator=nullptr) const;
  virtual Tensor randperm(int64_t n, Generator * generator=nullptr) const;
  virtual Tensor & random_(Tensor & self, int64_t from, int64_t to, Generator * generator=nullptr) const;
  virtual Tensor & random_(Tensor & self, int64_t to, Generator * generator=nullptr) const;
  virtual Tensor & random_(Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor & multinomial_out(Tensor & result, const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr) const;
  virtual Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr) const;
  virtual Tensor & uniform_(Tensor & self, double from=0, double to=1, Generator * generator=nullptr) const;
  virtual Tensor & normal_out(Tensor & output, const Tensor & mean, double std=1, Generator * generator=nullptr) const;
  virtual Tensor normal(const Tensor & mean, double std=1, Generator * generator=nullptr) const;
  virtual Tensor & normal_out(Tensor & output, double mean, const Tensor & std, Generator * generator=nullptr) const;
  virtual Tensor normal(double mean, const Tensor & std, Generator * generator=nullptr) const;
  virtual Tensor & normal_out(Tensor & output, const Tensor & mean, const Tensor & std, Generator * generator=nullptr) const;
  virtual Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator=nullptr) const;
  virtual Tensor & normal_(Tensor & self, double mean=0, double std=1, Generator * generator=nullptr) const;
  virtual Tensor & cauchy_(Tensor & self, double median=0, double sigma=1, Generator * generator=nullptr) const;
  virtual Tensor & log_normal_(Tensor & self, double mean=1, double std=2, Generator * generator=nullptr) const;
  virtual Tensor & exponential_(Tensor & self, double lambd=1, Generator * generator=nullptr) const;
  virtual Tensor & rand_out(Tensor & result, IntList size, Generator * generator=nullptr) const;
  virtual Tensor rand(IntList size, Generator * generator=nullptr) const;
  virtual Tensor & randn_out(Tensor & result, IntList size, Generator * generator=nullptr) const;
  virtual Tensor randn(IntList size, Generator * generator=nullptr) const;
  virtual Tensor & geometric_(Tensor & self, double p, Generator * generator=nullptr) const;
  virtual Tensor & bernoulli_out(Tensor & output, const Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor bernoulli(const Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor & _standard_gamma_out(Tensor & output, const Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor _standard_gamma(const Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor & _dirichlet_grad_out(Tensor & output, const Tensor & x, const Tensor & alpha, const Tensor & total) const;
  virtual Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total) const;
  virtual Tensor tensor(Storage & storage, int64_t storageOffset, IntList size, IntList stride={}) const;
  virtual Tensor tensor(IntList size) const;
  virtual Tensor tensor(IntList size, IntList stride) const;
  virtual Tensor tensor() const;
  virtual Tensor sparse_coo_tensor(IntList size) const;
  virtual Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntList size) const;
  virtual Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values) const;
  virtual Tensor alias(const Tensor & self) const;
  virtual Tensor & _copy_ignoring_overlaps_(Tensor & self, const Tensor & src) const;
  virtual Tensor & as_strided_out(Tensor & result, const Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1) const;
  virtual Tensor as_strided(const Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1) const;
  virtual Tensor & as_strided_(Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1) const;
  virtual Tensor & sparse_resize_(Tensor & self, IntList size, int64_t nDimI, int64_t nDimV) const;
  virtual Tensor & sparse_resize_and_clear_(Tensor & self, IntList size, int64_t nDimI, int64_t nDimV) const;
  virtual Tensor & _cat_out(Tensor & self, TensorList tensors, int64_t dim=0) const;
  virtual Tensor _cat(TensorList tensors, int64_t dim=0) const;
  virtual Tensor & reshape_(Tensor & self, IntList size, IntList stride) const;
  virtual Tensor _sparse_mask(const Tensor & self, SparseTensor mask) const;
  virtual Tensor to_dense(const Tensor & self) const;
  virtual int64_t _dimI(const Tensor & self) const;
  virtual int64_t _dimV(const Tensor & self) const;
  virtual int64_t _nnz(const Tensor & self) const;
  virtual Tensor coalesce(const Tensor & self) const;
  virtual bool is_coalesced(const Tensor & self) const;
  virtual Tensor _indices(const Tensor & self) const;
  virtual Tensor _values(const Tensor & self) const;
  virtual Tensor & binary_cross_entropy_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, bool reduce=true) const;
  virtual Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, bool reduce=true) const;
  virtual Tensor & binary_cross_entropy_forward_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce) const;
  virtual Tensor binary_cross_entropy_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce) const;
  virtual Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce) const;
  virtual Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce) const;
  virtual Tensor & kl_div_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor kl_div(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor & kl_div_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor kl_div_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & kl_div_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & l1_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor l1_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor & l1_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor l1_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & mse_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor mse_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor & mse_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor mse_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & multi_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true) const;
  virtual Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true) const;
  virtual Tensor & multi_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average) const;
  virtual Tensor multi_margin_loss_forward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average) const;
  virtual Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average) const;
  virtual Tensor multi_margin_loss_backward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average) const;
  virtual Tensor & multilabel_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual std::tuple<Tensor &,Tensor &> multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual std::tuple<Tensor,Tensor> multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce, const Tensor & is_target) const;
  virtual Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce, const Tensor & is_target) const;
  virtual Tensor & nll_loss_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true) const;
  virtual Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true) const;
  virtual std::tuple<Tensor &,Tensor &> nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce) const;
  virtual std::tuple<Tensor,Tensor> nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce) const;
  virtual Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight) const;
  virtual Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight) const;
  virtual Tensor & nll_loss2d_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true) const;
  virtual Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true) const;
  virtual std::tuple<Tensor &,Tensor &> nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce) const;
  virtual std::tuple<Tensor,Tensor> nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce) const;
  virtual Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight) const;
  virtual Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight) const;
  virtual Tensor & smooth_l1_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true) const;
  virtual Tensor & smooth_l1_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor smooth_l1_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce) const;
  virtual Tensor & soft_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true) const;
  virtual Tensor soft_margin_loss(const Tensor & self, const Tensor & target, bool size_average=true) const;
  virtual Tensor & soft_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average) const;
  virtual Tensor soft_margin_loss_forward(const Tensor & self, const Tensor & target, bool size_average) const;
  virtual Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & self, const Tensor & target, bool size_average) const;
  virtual Tensor soft_margin_loss_backward(const Tensor & self, const Tensor & target, bool size_average) const;
  virtual Tensor & elu_out(Tensor & output, const Tensor & self, Scalar alpha=1, Scalar scale=1) const;
  virtual Tensor elu(const Tensor & self, Scalar alpha=1, Scalar scale=1) const;
  virtual Tensor & elu_forward_out(Tensor & output, const Tensor & self, Scalar alpha, Scalar scale) const;
  virtual Tensor elu_forward(const Tensor & self, Scalar alpha, Scalar scale) const;
  virtual Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, const Tensor & output) const;
  virtual Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, const Tensor & output) const;
  virtual Tensor & elu_(Tensor & self, Scalar alpha=1, Scalar scale=1) const;
  virtual Tensor & elu_forward_(Tensor & self, Scalar alpha, Scalar scale) const;
  virtual Tensor & glu_out(Tensor & output, const Tensor & self, int64_t dim=-1) const;
  virtual Tensor glu(const Tensor & self, int64_t dim=-1) const;
  virtual Tensor & glu_forward_out(Tensor & output, const Tensor & self, int64_t dim) const;
  virtual Tensor glu_forward(const Tensor & self, int64_t dim) const;
  virtual Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim) const;
  virtual Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim) const;
  virtual Tensor & hardtanh_out(Tensor & output, const Tensor & self, Scalar min_val=-1, Scalar max_val=1) const;
  virtual Tensor hardtanh(const Tensor & self, Scalar min_val=-1, Scalar max_val=1) const;
  virtual Tensor & hardtanh_forward_out(Tensor & output, const Tensor & self, Scalar min_val, Scalar max_val) const;
  virtual Tensor hardtanh_forward(const Tensor & self, Scalar min_val, Scalar max_val) const;
  virtual Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val) const;
  virtual Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val) const;
  virtual Tensor & hardtanh_(Tensor & self, Scalar min_val=-1, Scalar max_val=1) const;
  virtual Tensor & hardtanh_forward_(Tensor & self, Scalar min_val, Scalar max_val) const;
  virtual Tensor & leaky_relu_out(Tensor & output, const Tensor & self, Scalar negative_slope=0.01) const;
  virtual Tensor leaky_relu(const Tensor & self, Scalar negative_slope=0.01) const;
  virtual Tensor & leaky_relu_forward_out(Tensor & output, const Tensor & self, Scalar negative_slope) const;
  virtual Tensor leaky_relu_forward(const Tensor & self, Scalar negative_slope) const;
  virtual Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope) const;
  virtual Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope) const;
  virtual Tensor & leaky_relu_(Tensor & self, Scalar negative_slope=0.01) const;
  virtual Tensor & leaky_relu_forward_(Tensor & self, Scalar negative_slope) const;
  virtual Tensor & log_sigmoid_out(Tensor & output, const Tensor & self) const;
  virtual Tensor log_sigmoid(const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self) const;
  virtual std::tuple<Tensor,Tensor> log_sigmoid_forward(const Tensor & self) const;
  virtual Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer) const;
  virtual Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer) const;
  virtual Tensor & log_softmax_out(Tensor & output, const Tensor & self, int64_t dim) const;
  virtual Tensor log_softmax(const Tensor & self, int64_t dim) const;
  virtual Tensor & log_softmax_forward_out(Tensor & output, const Tensor & self, int64_t dim) const;
  virtual Tensor log_softmax_forward(const Tensor & self, int64_t dim) const;
  virtual Tensor & log_softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output) const;
  virtual Tensor log_softmax_backward(const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output) const;
  virtual Tensor & prelu_out(Tensor & output, const Tensor & self, const Tensor & weight) const;
  virtual Tensor prelu(const Tensor & self, const Tensor & weight) const;
  virtual Tensor & prelu_forward_out(Tensor & output, const Tensor & self, const Tensor & weight) const;
  virtual Tensor prelu_forward(const Tensor & self, const Tensor & weight) const;
  virtual std::tuple<Tensor &,Tensor &> prelu_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight) const;
  virtual std::tuple<Tensor,Tensor> prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, std::array<bool,2> output_mask={{true, true}}) const;
  virtual Tensor & rrelu_with_noise_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr) const;
  virtual Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr) const;
  virtual Tensor & rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) const;
  virtual Tensor rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) const;
  virtual Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training) const;
  virtual Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training) const;
  virtual Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr) const;
  virtual Tensor & rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator) const;
  virtual Tensor & softmax_out(Tensor & output, const Tensor & self, int64_t dim) const;
  virtual Tensor softmax(const Tensor & self, int64_t dim) const;
  virtual Tensor & softmax_forward_out(Tensor & output, const Tensor & self, int64_t dim) const;
  virtual Tensor softmax_forward(const Tensor & self, int64_t dim) const;
  virtual Tensor & softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output) const;
  virtual Tensor softmax_backward(const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output) const;
  virtual Tensor & softplus_out(Tensor & output, const Tensor & self, Scalar beta=1, Scalar threshold=20) const;
  virtual Tensor softplus(const Tensor & self, Scalar beta=1, Scalar threshold=20) const;
  virtual Tensor & softplus_forward_out(Tensor & output, const Tensor & self, Scalar beta, Scalar threshold) const;
  virtual Tensor softplus_forward(const Tensor & self, Scalar beta, Scalar threshold) const;
  virtual Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output) const;
  virtual Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output) const;
  virtual Tensor & softshrink_out(Tensor & output, const Tensor & self, Scalar lambd=0.5) const;
  virtual Tensor softshrink(const Tensor & self, Scalar lambd=0.5) const;
  virtual Tensor & softshrink_forward_out(Tensor & output, const Tensor & self, Scalar lambd) const;
  virtual Tensor softshrink_forward(const Tensor & self, Scalar lambd) const;
  virtual Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd) const;
  virtual Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd) const;
  virtual Tensor & threshold_out(Tensor & output, const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor threshold(const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor & threshold_forward_out(Tensor & output, const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor threshold_forward(const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor & threshold_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor & threshold_forward_(Tensor & self, Scalar threshold, Scalar value) const;
  virtual Tensor & adaptive_avg_pool2d_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor adaptive_avg_pool2d(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_avg_pool2d_forward_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor adaptive_avg_pool2d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self) const;
  virtual Tensor adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self) const;
  virtual Tensor & adaptive_avg_pool3d_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor adaptive_avg_pool3d(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_avg_pool3d_forward_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor adaptive_avg_pool3d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self) const;
  virtual Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self) const;
  virtual std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor,Tensor> adaptive_max_pool2d(const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor,Tensor> adaptive_max_pool2d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices) const;
  virtual Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices) const;
  virtual std::tuple<Tensor &,Tensor &> adaptive_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor,Tensor> adaptive_max_pool3d(const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor &,Tensor &> adaptive_max_pool3d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor,Tensor> adaptive_max_pool3d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices) const;
  virtual Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices) const;
  virtual Tensor & avg_pool2d_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false) const;
  virtual Tensor avg_pool2d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false) const;
  virtual Tensor & avg_pool2d_forward_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor avg_pool2d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor & avg_pool3d_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false) const;
  virtual Tensor avg_pool3d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false) const;
  virtual Tensor & avg_pool3d_forward_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor avg_pool3d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad) const;
  virtual std::tuple<Tensor &,Tensor &> fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples) const;
  virtual std::tuple<Tensor,Tensor> fractional_max_pool2d(const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples) const;
  virtual std::tuple<Tensor &,Tensor &> fractional_max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples) const;
  virtual std::tuple<Tensor,Tensor> fractional_max_pool2d_forward(const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples) const;
  virtual Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & indices) const;
  virtual Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & indices) const;
  virtual std::tuple<Tensor &,Tensor &> max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false) const;
  virtual std::tuple<Tensor,Tensor> max_pool2d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false) const;
  virtual std::tuple<Tensor &,Tensor &> max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode) const;
  virtual std::tuple<Tensor,Tensor> max_pool2d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode) const;
  virtual Tensor & max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices) const;
  virtual Tensor max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices) const;
  virtual std::tuple<Tensor &,Tensor &> max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false) const;
  virtual std::tuple<Tensor,Tensor> max_pool3d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false) const;
  virtual std::tuple<Tensor &,Tensor &> max_pool3d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode) const;
  virtual std::tuple<Tensor,Tensor> max_pool3d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode) const;
  virtual Tensor & max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices) const;
  virtual Tensor max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices) const;
  virtual Tensor & max_unpool2d_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor & max_unpool2d_forward_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor max_unpool2d_forward(const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size) const;
  virtual Tensor & max_unpool3d_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor & max_unpool3d_forward_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor max_unpool3d_forward(const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding) const;
  virtual Tensor & reflection_pad1d_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad1d(const Tensor & self, IntList padding) const;
  virtual Tensor & reflection_pad1d_forward_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad1d_forward(const Tensor & self, IntList padding) const;
  virtual Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor & reflection_pad2d_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad2d(const Tensor & self, IntList padding) const;
  virtual Tensor & reflection_pad2d_forward_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad2d_forward(const Tensor & self, IntList padding) const;
  virtual Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad1d_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad1d(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad1d_forward_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad1d_forward(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad2d_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad2d(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad2d_forward_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad2d_forward(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad3d_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad3d(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad3d_forward_out(Tensor & output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad3d_forward(const Tensor & self, IntList padding) const;
  virtual Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntList padding) const;
  virtual Tensor & upsample_linear1d_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_linear1d(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_linear1d_forward_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_linear1d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor upsample_linear1d_backward(const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor & upsample_bilinear2d_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_bilinear2d(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_bilinear2d_forward_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_bilinear2d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor & upsample_trilinear3d_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_trilinear3d(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_trilinear3d_forward_out(Tensor & output, const Tensor & self, IntList output_size) const;
  virtual Tensor upsample_trilinear3d_forward(const Tensor & self, IntList output_size) const;
  virtual Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntList output_size, IntList input_size) const;
  virtual Tensor & upsample_nearest1d_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest1d(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest1d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest1d_forward(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest1d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest2d_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest2d(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest2d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest2d_forward(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest2d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest3d_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest3d(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest3d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest3d_forward(const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor upsample_nearest3d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor) const;
  virtual Tensor & _sigmoid_out(Tensor & output, const Tensor & self) const;
  virtual Tensor _sigmoid(const Tensor & self) const;
  virtual Tensor & _sigmoid_forward_out(Tensor & output, const Tensor & self) const;
  virtual Tensor _sigmoid_forward(const Tensor & self) const;
  virtual Tensor & _sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output) const;
  virtual Tensor _sigmoid_backward(const Tensor & grad_output, const Tensor & output) const;
  virtual Tensor & _tanh_out(Tensor & output, const Tensor & self) const;
  virtual Tensor _tanh(const Tensor & self) const;
  virtual Tensor & _tanh_forward_out(Tensor & output, const Tensor & self) const;
  virtual Tensor _tanh_forward(const Tensor & self) const;
  virtual Tensor & _tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output) const;
  virtual Tensor _tanh_backward(const Tensor & grad_output, const Tensor & output) const;
  virtual Tensor & thnn_batch_norm_out(Tensor & output, const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps) const;
  virtual Tensor thnn_batch_norm(const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_batch_norm_forward_out(Tensor & output, Tensor & save_mean, Tensor & save_std, const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_batch_norm_forward(const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_batch_norm_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_batch_norm_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv_transpose2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1) const;
  virtual Tensor thnn_conv_transpose2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv_transpose3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1) const;
  virtual Tensor thnn_conv_transpose3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0) const;
  virtual Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv_depthwise2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual Tensor & thnn_conv_depthwise2d_forward_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual Tensor thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor &,Tensor &> thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor,Tensor> thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, std::array<bool,2> output_mask={{true, true}}) const;
  virtual Tensor & thnn_conv3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0) const;
  virtual Tensor thnn_conv3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv_dilated2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual Tensor thnn_conv_dilated2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor & thnn_conv_dilated3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual Tensor thnn_conv_dilated3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated3d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation) const;
  virtual std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones) const;
  virtual std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}}) const;
  virtual Tensor adaptive_avg_pool1d(const Tensor & self, IntList output_size) const;
  virtual std::tuple<Tensor,Tensor> adaptive_max_pool1d(const Tensor & self, IntList output_size) const;
  virtual bool allclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08) const;
  virtual Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled) const;
  virtual Tensor & bernoulli_(Tensor & self, const Tensor & p, Generator * generator=nullptr) const;
  virtual Tensor & bernoulli_(Tensor & self, double p=0.5, Generator * generator=nullptr) const;
  virtual Tensor cat(TensorList tensors, int64_t dim=0) const;
  virtual Tensor & cat_out(Tensor & result, TensorList tensors, int64_t dim=0) const;
  virtual Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual Tensor & sspaddmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
  virtual std::vector<Tensor> chunk(const Tensor & self, int64_t chunks, int64_t dim=0) const;
  virtual bool cudnn_is_acceptable(const Tensor & self) const;
  virtual Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups) const;
  virtual Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) const;
  virtual Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding) const;
  virtual std::tuple<Tensor,Tensor,Tensor> _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array<bool,3> output_mask) const;
  virtual Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1) const;
  virtual Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1) const;
  virtual Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1) const;
  virtual Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad) const;
  virtual std::tuple<Tensor,Tensor,Tensor> conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad) const;
  virtual Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1) const;
  virtual Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1) const;
  virtual Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1) const;
  virtual Tensor cudnn_affine_grid_generator(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) const;
  virtual Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) const;
  virtual std::tuple<Tensor,Tensor,Tensor> cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon) const;
  virtual std::tuple<Tensor,Tensor,Tensor> cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon) const;
  virtual Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual Tensor cudnn_convolution_backward_input(IntList self_size, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual std::tuple<Tensor,Tensor,Tensor> cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const;
  virtual Tensor cudnn_convolution_backward_bias(const Tensor & grad_output) const;
  virtual Tensor cudnn_convolution_backward_weight(IntList weight_size, const Tensor & grad_output, const Tensor & self, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual std::tuple<Tensor,Tensor,Tensor> cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask) const;
  virtual Tensor cudnn_convolution_transpose_backward_bias(const Tensor & grad_output) const;
  virtual Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual Tensor cudnn_convolution_transpose_backward_weight(IntList weight_size, const Tensor & grad_output, const Tensor & self, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic) const;
  virtual Tensor cudnn_grid_sampler(const Tensor & self, const Tensor & grid) const;
  virtual std::tuple<Tensor,Tensor> cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output) const;
  virtual Tensor det(const Tensor & self) const;
  virtual std::tuple<Tensor,Tensor,Tensor,Tensor> _det_with_svd(const Tensor & self) const;
  virtual Tensor dot(const Tensor & self, const Tensor & tensor) const;
  virtual Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) const;
  virtual Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) const;
  virtual Tensor embedding_dense_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const;
  virtual Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type) const;
  virtual Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) const;
  virtual Tensor empty_like(const Tensor & self) const;
  virtual std::tuple<Tensor,Tensor,Tensor> embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false) const;
  virtual Tensor embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse) const;
  virtual Tensor embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const;
  virtual Tensor embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode) const;
  virtual Tensor expand(const Tensor & self, IntList size) const;
  virtual Tensor expand_as(const Tensor & self, const Tensor & other) const;
  virtual Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin, bool size_average, bool reduce) const;
  virtual Tensor ger(const Tensor & self, const Tensor & vec2) const;
  virtual Tensor & ger_out(Tensor & result, const Tensor & self, const Tensor & vec2) const;
  virtual Tensor index(const Tensor & self, TensorList indices) const;
  virtual Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values) const;
  virtual bool is_cuda(const Tensor & self) const;
  virtual bool is_distributed(const Tensor & self) const;
  virtual bool is_floating_point(const Tensor & self) const;
  virtual bool is_nonzero(const Tensor & self) const;
  virtual bool is_same_size(const Tensor & self, const Tensor & other) const;
  virtual bool is_signed(const Tensor & self) const;
  virtual bool is_sparse(const Tensor & self) const;
  virtual Tensor matmul(const Tensor & self, const Tensor & other) const;
  virtual std::tuple<Tensor,Tensor> max_pool1d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false) const;
  virtual Tensor mm(const Tensor & self, const Tensor & mat2) const;
  virtual Tensor & mm_out(Tensor & result, const Tensor & self, const Tensor & mat2) const;
  virtual Tensor mv(const Tensor & self, const Tensor & vec) const;
  virtual Tensor & mv_out(Tensor & result, const Tensor & self, const Tensor & vec) const;
  virtual Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length) const;
  virtual Tensor permute(const Tensor & self, IntList dims) const;
  virtual Tensor pin_memory(const Tensor & self) const;
  virtual Tensor rand_like(const Tensor & self) const;
  virtual Tensor randn_like(const Tensor & self) const;
  virtual Tensor repeat(const Tensor & self, IntList repeats) const;
  virtual std::tuple<Tensor,Tensor> RoiPooling2d_forward(const Tensor & input, const Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale) const;
  virtual Tensor RoiPooling2d_backward(const Tensor & input, const Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale, const Tensor & gradOutput, const Tensor & argmaxes) const;
  virtual Tensor rrelu(const Tensor & self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr) const;
  virtual Tensor & rrelu_(Tensor & self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr) const;
  virtual Tensor select(const Tensor & self, int64_t dim, int64_t index) const;
  virtual Tensor selu(const Tensor & self) const;
  virtual Tensor & selu_(Tensor & self) const;
  virtual int64_t size(const Tensor & self, int64_t dim) const;
  virtual Tensor slice(const Tensor & self, int64_t dim=0, int64_t start=0, int64_t end=9223372036854775807, int64_t step=1) const;
  virtual std::vector<Tensor> split(const Tensor & self, int64_t split_size, int64_t dim=0) const;
  virtual Tensor squeeze(const Tensor & self) const;
  virtual Tensor squeeze(const Tensor & self, int64_t dim) const;
  virtual Tensor & squeeze_(Tensor & self) const;
  virtual Tensor & squeeze_(Tensor & self, int64_t dim) const;
  virtual Tensor stack(TensorList tensors, int64_t dim=0) const;
  virtual Tensor & stack_out(Tensor & result, TensorList tensors, int64_t dim=0) const;
  virtual Tensor stft(const Tensor & self, int64_t frame_length, int64_t hop, int64_t fft_size, bool return_onesided=true, const Tensor & window={}, int64_t pad_end=0) const;
  virtual int64_t stride(const Tensor & self, int64_t dim) const;
  virtual Tensor & transpose_(Tensor & self, int64_t dim0, int64_t dim1) const;
  virtual Tensor & t_(Tensor & self) const;
  virtual Tensor type_as(const Tensor & self, const Tensor & other) const;
  virtual Tensor unsqueeze(const Tensor & self, int64_t dim) const;
  virtual Tensor & unsqueeze_(Tensor & self, int64_t dim) const;
  virtual Tensor view_as(const Tensor & self, const Tensor & other) const;
  virtual Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other) const;
  virtual Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other) const;
  virtual Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output) const;
  virtual Tensor poisson(const Tensor & self, Generator * generator=nullptr) const;
  virtual Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional) const;
  virtual std::tuple<Tensor,Tensor,Tensor,Tensor,Tensor> _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, const Tensor & dropout_state) const;
  virtual std::tuple<Tensor,Tensor,Tensor,std::vector<Tensor>> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array<bool,4> output_mask) const;
protected:
  Context* context;
};


}
